import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

df=pd.read_csv(r"F:\Indian Agriculture Dataset\ICRISAT.csv")

df

	Dist Code	Year	State Code	State Name	Dist Name	RICE AREA (1000 ha)	RICE PRODUCTION (1000 tons)	RICE YIELD (Kg per ha)	WHEAT AREA (1000 ha)	WHEAT PRODUCTION (1000 tons)	...	SUGARCANE YIELD (Kg per ha)	COTTON AREA (1000 ha)	COTTON PRODUCTION (1000 tons)	COTTON YIELD (Kg per ha)	FRUITS AREA (1000 ha)	VEGETABLES AREA (1000 ha)	FRUITS AND VEGETABLES AREA (1000 ha)	POTATOES AREA (1000 ha)	ONION AREA (1000 ha)	FODDER AREA (1000 ha)
0	1	1966	14	Chhattisgarh	Durg	548.00	185.00	337.59	44.00	20.00	...	1777.78	0.0	0.0	0.0	5.95	6.64	12.59	0.01	0.60	0.47
1	1	1967	14	Chhattisgarh	Durg	547.00	409.00	747.71	50.00	26.00	...	1500.00	0.0	0.0	0.0	5.77	7.24	13.02	0.01	0.56	1.23
2	1	1968	14	Chhattisgarh	Durg	556.30	468.00	841.27	53.70	30.00	...	1000.00	0.0	0.0	0.0	5.41	7.40	12.81	0.10	0.58	1.02
3	1	1969	14	Chhattisgarh	Durg	563.40	400.80	711.40	49.40	26.50	...	1900.00	0.0	0.0	0.0	5.52	7.16	12.69	0.01	0.56	0.84
4	1	1970	14	Chhattisgarh	Durg	571.60	473.60	828.55	44.20	29.00	...	2000.00	0.0	0.0	0.0	5.45	7.19	12.64	0.02	0.52	0.42
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16141	917	2013	15	Jharkhand	Singhbhum	267.06	579.70	2170.67	1.53	1.85	...	0.00	0.0	0.0	0.0	0.00	9.02	9.03	0.48	0.08	0.00
16142	917	2014	15	Jharkhand	Singhbhum	256.33	586.63	2288.57	5.36	6.65	...	0.00	0.0	0.0	0.0	0.00	8.63	8.64	0.46	0.04	0.00
16143	917	2015	15	Jharkhand	Singhbhum	263.21	264.71	1005.70	1.99	1.82	...	0.00	0.0	0.0	0.0	0.00	6.84	6.85	0.42	0.01	0.00
16144	917	2016	15	Jharkhand	Singhbhum	224.05	319.01	1423.84	0.38	0.83	...	0.00	0.0	0.0	0.0	0.03	6.40	6.43	0.03	0.01	0.00
16145	917	2017	15	Jharkhand	Singhbhum	386.91	669.97	1731.62	0.00	0.00	...	0.00	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	0.00

16146 rows × 80 columns

df.columns

Index(['Dist Code', 'Year', 'State Code', 'State Name', 'Dist Name',
       'RICE AREA (1000 ha)', 'RICE PRODUCTION (1000 tons)',
       'RICE YIELD (Kg per ha)', 'WHEAT AREA (1000 ha)',
       'WHEAT PRODUCTION (1000 tons)', 'WHEAT YIELD (Kg per ha)',
       'KHARIF SORGHUM AREA (1000 ha)',
       'KHARIF SORGHUM PRODUCTION (1000 tons)',
       'KHARIF SORGHUM YIELD (Kg per ha)', 'RABI SORGHUM AREA (1000 ha)',
       'RABI SORGHUM PRODUCTION (1000 tons)', 'RABI SORGHUM YIELD (Kg per ha)',
       'SORGHUM AREA (1000 ha)', 'SORGHUM PRODUCTION (1000 tons)',
       'SORGHUM YIELD (Kg per ha)', 'PEARL MILLET AREA (1000 ha)',
       'PEARL MILLET PRODUCTION (1000 tons)', 'PEARL MILLET YIELD (Kg per ha)',
       'MAIZE AREA (1000 ha)', 'MAIZE PRODUCTION (1000 tons)',
       'MAIZE YIELD (Kg per ha)', 'FINGER MILLET AREA (1000 ha)',
       'FINGER MILLET PRODUCTION (1000 tons)',
       'FINGER MILLET YIELD (Kg per ha)', 'BARLEY AREA (1000 ha)',
       'BARLEY PRODUCTION (1000 tons)', 'BARLEY YIELD (Kg per ha)',
       'CHICKPEA AREA (1000 ha)', 'CHICKPEA PRODUCTION (1000 tons)',
       'CHICKPEA YIELD (Kg per ha)', 'PIGEONPEA AREA (1000 ha)',
       'PIGEONPEA PRODUCTION (1000 tons)', 'PIGEONPEA YIELD (Kg per ha)',
       'MINOR PULSES AREA (1000 ha)', 'MINOR PULSES PRODUCTION (1000 tons)',
       'MINOR PULSES YIELD (Kg per ha)', 'GROUNDNUT AREA (1000 ha)',
       'GROUNDNUT PRODUCTION (1000 tons)', 'GROUNDNUT YIELD (Kg per ha)',
       'SESAMUM AREA (1000 ha)', 'SESAMUM PRODUCTION (1000 tons)',
       'SESAMUM YIELD (Kg per ha)', 'RAPESEED AND MUSTARD AREA (1000 ha)',
       'RAPESEED AND MUSTARD PRODUCTION (1000 tons)',
       'RAPESEED AND MUSTARD YIELD (Kg per ha)', 'SAFFLOWER AREA (1000 ha)',
       'SAFFLOWER PRODUCTION (1000 tons)', 'SAFFLOWER YIELD (Kg per ha)',
       'CASTOR AREA (1000 ha)', 'CASTOR PRODUCTION (1000 tons)',
       'CASTOR YIELD (Kg per ha)', 'LINSEED AREA (1000 ha)',
       'LINSEED PRODUCTION (1000 tons)', 'LINSEED YIELD (Kg per ha)',
       'SUNFLOWER AREA (1000 ha)', 'SUNFLOWER PRODUCTION (1000 tons)',
       'SUNFLOWER YIELD (Kg per ha)', 'SOYABEAN AREA (1000 ha)',
       'SOYABEAN PRODUCTION (1000 tons)', 'SOYABEAN YIELD (Kg per ha)',
       'OILSEEDS AREA (1000 ha)', 'OILSEEDS PRODUCTION (1000 tons)',
       'OILSEEDS YIELD (Kg per ha)', 'SUGARCANE AREA (1000 ha)',
       'SUGARCANE PRODUCTION (1000 tons)', 'SUGARCANE YIELD (Kg per ha)',
       'COTTON AREA (1000 ha)', 'COTTON PRODUCTION (1000 tons)',
       'COTTON YIELD (Kg per ha)', 'FRUITS AREA (1000 ha)',
       'VEGETABLES AREA (1000 ha)', 'FRUITS AND VEGETABLES AREA (1000 ha)',
       'POTATOES AREA (1000 ha)', 'ONION AREA (1000 ha)',
       'FODDER AREA (1000 ha)'],
      dtype='object')

# Normally, pandas only shows a limited number of rows when you print something. # pd.option_context temporarily changes this behavior so pandas shows all rows (in this case, all 80 columns). # After the with block, pandas goes back to its default settings.

with pd.option_context('display.max_rows', None):
    print(df.isnull().sum())

Dist Code                                      0
Year                                           0
State Code                                     0
State Name                                     0
Dist Name                                      0
RICE AREA (1000 ha)                            0
RICE PRODUCTION (1000 tons)                    0
RICE YIELD (Kg per ha)                         0
WHEAT AREA (1000 ha)                           0
WHEAT PRODUCTION (1000 tons)                   0
WHEAT YIELD (Kg per ha)                        0
KHARIF SORGHUM AREA (1000 ha)                  0
KHARIF SORGHUM PRODUCTION (1000 tons)          0
KHARIF SORGHUM YIELD (Kg per ha)               0
RABI SORGHUM AREA (1000 ha)                    0
RABI SORGHUM PRODUCTION (1000 tons)            0
RABI SORGHUM YIELD (Kg per ha)                 0
SORGHUM AREA (1000 ha)                         0
SORGHUM PRODUCTION (1000 tons)                 0
SORGHUM YIELD (Kg per ha)                      0
PEARL MILLET AREA (1000 ha)                    0
PEARL MILLET PRODUCTION (1000 tons)            0
PEARL MILLET YIELD (Kg per ha)                 0
MAIZE AREA (1000 ha)                           0
MAIZE PRODUCTION (1000 tons)                   0
MAIZE YIELD (Kg per ha)                        0
FINGER MILLET AREA (1000 ha)                   0
FINGER MILLET PRODUCTION (1000 tons)           0
FINGER MILLET YIELD (Kg per ha)                0
BARLEY AREA (1000 ha)                          0
BARLEY PRODUCTION (1000 tons)                  0
BARLEY YIELD (Kg per ha)                       0
CHICKPEA AREA (1000 ha)                        0
CHICKPEA PRODUCTION (1000 tons)                0
CHICKPEA YIELD (Kg per ha)                     0
PIGEONPEA AREA (1000 ha)                       0
PIGEONPEA PRODUCTION (1000 tons)               0
PIGEONPEA YIELD (Kg per ha)                    0
MINOR PULSES AREA (1000 ha)                    0
MINOR PULSES PRODUCTION (1000 tons)            0
MINOR PULSES YIELD (Kg per ha)                 0
GROUNDNUT AREA (1000 ha)                       0
GROUNDNUT PRODUCTION (1000 tons)               0
GROUNDNUT YIELD (Kg per ha)                    0
SESAMUM AREA (1000 ha)                         0
SESAMUM PRODUCTION (1000 tons)                 0
SESAMUM YIELD (Kg per ha)                      0
RAPESEED AND MUSTARD AREA (1000 ha)            0
RAPESEED AND MUSTARD PRODUCTION (1000 tons)    0
RAPESEED AND MUSTARD YIELD (Kg per ha)         0
SAFFLOWER AREA (1000 ha)                       0
SAFFLOWER PRODUCTION (1000 tons)               0
SAFFLOWER YIELD (Kg per ha)                    0
CASTOR AREA (1000 ha)                          0
CASTOR PRODUCTION (1000 tons)                  0
CASTOR YIELD (Kg per ha)                       0
LINSEED AREA (1000 ha)                         0
LINSEED PRODUCTION (1000 tons)                 0
LINSEED YIELD (Kg per ha)                      0
SUNFLOWER AREA (1000 ha)                       0
SUNFLOWER PRODUCTION (1000 tons)               0
SUNFLOWER YIELD (Kg per ha)                    0
SOYABEAN AREA (1000 ha)                        0
SOYABEAN PRODUCTION (1000 tons)                0
SOYABEAN YIELD (Kg per ha)                     0
OILSEEDS AREA (1000 ha)                        0
OILSEEDS PRODUCTION (1000 tons)                0
OILSEEDS YIELD (Kg per ha)                     0
SUGARCANE AREA (1000 ha)                       0
SUGARCANE PRODUCTION (1000 tons)               0
SUGARCANE YIELD (Kg per ha)                    0
COTTON AREA (1000 ha)                          0
COTTON PRODUCTION (1000 tons)                  0
COTTON YIELD (Kg per ha)                       0
FRUITS AREA (1000 ha)                          0
VEGETABLES AREA (1000 ha)                      0
FRUITS AND VEGETABLES AREA (1000 ha)           0
POTATOES AREA (1000 ha)                        0
ONION AREA (1000 ha)                           0
FODDER AREA (1000 ha)                          0
dtype: int64

df.duplicated().sum()

0

df['Year'].unique()

array([1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
       1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017], dtype=int64)

RICE

TOP RICE PRODUCTION STATE

rice_state=df.groupby('State Name',as_index=False)['RICE PRODUCTION (1000 tons)'].sum().sort_values(by='RICE PRODUCTION (1000 tons)',ascending=False).reset_index(drop='index')

rice_state

	State Name	RICE PRODUCTION (1000 tons)
0	West Bengal	544232.26
1	Uttar Pradesh	445597.62
2	Punjab	335040.10
3	Andhra Pradesh	315185.40
4	Tamil Nadu	291201.51
5	Orissa	282532.93
6	Bihar	231759.00
7	Chhattisgarh	208208.22
8	Assam	165205.95
9	Telangana	152911.74
10	Karnataka	146921.56
11	Maharashtra	116528.76
12	Haryana	107664.90
13	Jharkhand	76435.78
14	Madhya Pradesh	76342.20
15	Kerala	49567.07
16	Gujarat	48713.35
17	Uttarakhand	22124.13
18	Rajasthan	9468.42
19	Himachal Pradesh	5426.01

rice_state_d=rice_state.head(10)
fig=px.bar(rice_state_d,x="State Name",y="RICE PRODUCTION (1000 tons)",
          title="STATE AND RICE PRODUCTION (1996-2017)",
          labels={"State Name":"State Name","RICE PRODUCTION (1000 tons)":"RICE PRODUCTION"},
           color="State Name",
           height=500,
           width=600
          )
fig.update_layout(showlegend=False, title_x=0.5,template="simple_white")

fig.show()

West Bengal leads in rice production, producing over 500,000 tons, making it the top rice-producing state from 1996–2017. Uttar Pradesh and Punjab follow, with significant but lower production levels than West Bengal. States like Andhra Pradesh, Tamil Nadu, and Orissa maintain moderate production levels

TOP 10 WEST BENGAL DISTRICT WITH RICE PRODUCTION

rice_dist_wb=df[df['State Name']== 'West Bengal']

rice_dist_wb_data=rice_dist_wb.groupby('Dist Name',as_index=False)['RICE PRODUCTION (1000 tons)'].sum().sort_values(by='RICE PRODUCTION (1000 tons)',ascending=False).reset_index(drop='index')

rice_dist_wb_data

	Dist Name	RICE PRODUCTION (1000 tons)
0	Midnapur	98868.28
1	Burdwan	71422.15
2	24 Parganas	63011.99
3	Birbhum	42213.81
4	Bankura	40165.20
5	Murshidabad	37261.24
6	West Dinajpur	35224.87
7	Hooghly	30962.21
8	Nadia	28016.30
9	Purulia	23147.50
10	Malda	22497.83
11	Cooch Behar	20949.15
12	Jalpaiguri	17372.00
13	Howrah	10264.53
14	Darjeeling	2855.20

rice_dist_wb_data=rice_dist_wb_data.head(10)
fig=px.bar(rice_dist_wb_data,x="Dist Name",y="RICE PRODUCTION (1000 tons)",
          title="WB DISTRICT AND RICE PRODUCTION (1996-2017)",
          labels={"Dist Name":"Distrcit","RICE PRODUCTION (1000 tons)":"RICE PRODUCTION"},
           color="Dist Name",
           height=500,
           width=600
          )
fig.update_layout(showlegend=False, title_x=0.5,template="simple_white")

fig.show()

IN WEST BENGAL MIDNAPUR HOLDS HIGHEST RICE PRODUCTION RECORDS 98K TONS ,FOLLOWED BY BURDWAN 71K TONS AND 24 PARGANAS 63K TONS

YEAR WISE RICE PRODUCTION IN STATES

df_filtered=df[['State Name','Year','RICE PRODUCTION (1000 tons)']]

df_filtered_data=df_filtered.groupby(['State Name','Year'],as_index=False)['RICE PRODUCTION (1000 tons)'].sum().sort_values(by=['State Name','Year'],ascending=False).reset_index(drop='index')

df_filtered_data

	State Name	Year	RICE PRODUCTION (1000 tons)
0	West Bengal	2017	15658.94
1	West Bengal	2016	15106.32
2	West Bengal	2015	15948.26
3	West Bengal	2014	15927.20
4	West Bengal	2013	15376.88
...	...	...	...
1033	Andhra Pradesh	1970	3434.70
1034	Andhra Pradesh	1969	3085.60
1035	Andhra Pradesh	1968	2754.00
1036	Andhra Pradesh	1967	3323.00
1037	Andhra Pradesh	1966	3214.00

1038 rows × 3 columns

unique_state=df_filtered_data['State Name'].unique()[:10]
filtered_data=df_filtered_data[df_filtered_data['State Name'].isin(unique_state)]
filtered_data

	State Name	Year	RICE PRODUCTION (1000 tons)
0	West Bengal	2017	15658.94
1	West Bengal	2016	15106.32
2	West Bengal	2015	15948.26
3	West Bengal	2014	15927.20
4	West Bengal	2013	15376.88
...	...	...	...
515	Madhya Pradesh	1970	945.40
516	Madhya Pradesh	1969	921.20
517	Madhya Pradesh	1968	668.60
518	Madhya Pradesh	1967	829.40
519	Madhya Pradesh	1966	374.20

520 rows × 3 columns

fig=px.line(
    
    filtered_data,
    x='Year',
    y='RICE PRODUCTION (1000 tons)',
    title='RICE PRODUCTION PER YEAR',
    markers=True,
    color='State Name',
    labels={'Year':'Year','RICE PRODUCTION (1000 tons)':'RICE PRODUCTION'},
    height=800,
    width=1100
    
)

fig.update_layout(
    template='plotly_white'
)

fig.show()

According to the data, both West Bengal and Uttar Pradesh outperformed expectations in 2017, each producing over 15,000 tons of rice. Punjab, while still significant, recorded a production of around 13,000 tons. Meanwhile, Orissa's rice production showed notable fluctuations, highlighting an inconsistency in its output

STATE AND RICE YIELD

rice_state_yield=df.groupby('State Name',as_index=False)['RICE YIELD (Kg per ha)'].sum().sort_values(by='RICE YIELD (Kg per ha)',ascending=False).reset_index(drop='index')

rice_state_yield=rice_state_yield.head(10)
rice_state_yield

	State Name	RICE YIELD (Kg per ha)
0	Uttar Pradesh	3767642.74
1	Karnataka	2125721.28
2	Punjab	1773440.44
3	Tamil Nadu	1755871.99
4	Madhya Pradesh	1575672.54
5	Maharashtra	1463057.73
6	West Bengal	1443769.07
7	Andhra Pradesh	1337079.18
8	Telangana	1020098.92
9	Kerala	995867.63

fig=px.bar(rice_state_yield,x="State Name",y="RICE YIELD (Kg per ha)",
          title="STATE AND RICE YIELD(1996-2017)",
          labels={"State Name":"State","RICE YIELD (Kg per ha)":"RICE YIELD"},
           color="RICE YIELD (Kg per ha)",
           color_continuous_scale='turbo',
           height=500,
           width=600
          )
fig.update_layout( title_x=0.5,template="simple_white")
fig.update_coloraxes(showscale=False)  # Hides the color bar


fig.show()

Uttar Pradesh leads in rice yield, producing over 3.7M tons, making it the top rice-yielding state from 1996–2017. Karnataka and Punjab follow, with significant .

RICE PRODUCTION VS RICE YIELD

rice_state_new=rice_state
rice_state_new

	State Name	RICE PRODUCTION (1000 tons)
0	West Bengal	544232.26
1	Uttar Pradesh	445597.62
2	Punjab	335040.10
3	Andhra Pradesh	315185.40
4	Tamil Nadu	291201.51
5	Orissa	282532.93
6	Bihar	231759.00
7	Chhattisgarh	208208.22
8	Assam	165205.95
9	Telangana	152911.74
10	Karnataka	146921.56
11	Maharashtra	116528.76
12	Haryana	107664.90
13	Jharkhand	76435.78
14	Madhya Pradesh	76342.20
15	Kerala	49567.07
16	Gujarat	48713.35
17	Uttarakhand	22124.13
18	Rajasthan	9468.42
19	Himachal Pradesh	5426.01

marge_table=pd.merge(rice_state_yield,rice_state_new,on='State Name',how='left')

marge_table

	State Name	RICE YIELD (Kg per ha)	RICE PRODUCTION (1000 tons)
0	Uttar Pradesh	3767642.74	445597.62
1	Karnataka	2125721.28	146921.56
2	Punjab	1773440.44	335040.10
3	Tamil Nadu	1755871.99	291201.51
4	Madhya Pradesh	1575672.54	76342.20
5	Maharashtra	1463057.73	116528.76
6	West Bengal	1443769.07	544232.26
7	Andhra Pradesh	1337079.18	315185.40
8	Telangana	1020098.92	152911.74
9	Kerala	995867.63	49567.07

marge_table_data=marge_table.melt(id_vars='State Name',var_name='Metric',value_name='Count')

marge_table_data

	State Name	Metric	Count
0	Uttar Pradesh	RICE YIELD (Kg per ha)	3767642.74
1	Karnataka	RICE YIELD (Kg per ha)	2125721.28
2	Punjab	RICE YIELD (Kg per ha)	1773440.44
3	Tamil Nadu	RICE YIELD (Kg per ha)	1755871.99
4	Madhya Pradesh	RICE YIELD (Kg per ha)	1575672.54
5	Maharashtra	RICE YIELD (Kg per ha)	1463057.73
6	West Bengal	RICE YIELD (Kg per ha)	1443769.07
7	Andhra Pradesh	RICE YIELD (Kg per ha)	1337079.18
8	Telangana	RICE YIELD (Kg per ha)	1020098.92
9	Kerala	RICE YIELD (Kg per ha)	995867.63
10	Uttar Pradesh	RICE PRODUCTION (1000 tons)	445597.62
11	Karnataka	RICE PRODUCTION (1000 tons)	146921.56
12	Punjab	RICE PRODUCTION (1000 tons)	335040.10
13	Tamil Nadu	RICE PRODUCTION (1000 tons)	291201.51
14	Madhya Pradesh	RICE PRODUCTION (1000 tons)	76342.20
15	Maharashtra	RICE PRODUCTION (1000 tons)	116528.76
16	West Bengal	RICE PRODUCTION (1000 tons)	544232.26
17	Andhra Pradesh	RICE PRODUCTION (1000 tons)	315185.40
18	Telangana	RICE PRODUCTION (1000 tons)	152911.74
19	Kerala	RICE PRODUCTION (1000 tons)	49567.07

fig=px.bar(
    marge_table_data,
    x='State Name',
    y='Count',
    color='Metric',
    barmode='group',
     width=900,
    height=400,
    color_discrete_sequence=['#FF4500', '#228B22']
)
fig.update_layout(title="PRODUCTION VS YIELD",
                  title_x=0.2
)

fig.show()

Uttar Pradesh leads in Rice Yield (Kg per hectare), showcasing the highest efficiency in rice cultivation. West Bengal has high Rice Production (1000 tons), However, its Rice Yield (Kg per hectare) is lower than that of Uttar Pradesh

WHEAT

TOP WHEAT PRODUCTION STATE

wheat_state=df.groupby('State Name',as_index=False)['WHEAT PRODUCTION (1000 tons)'].sum().sort_values(by='WHEAT PRODUCTION (1000 tons)',ascending=False).reset_index(drop='index')

wheat_state

	State Name	WHEAT PRODUCTION (1000 tons)
0	Uttar Pradesh	970210.07
1	Punjab	593848.90
2	Haryana	348429.60
3	Madhya Pradesh	338644.25
4	Rajasthan	265994.99
5	Bihar	176250.30
6	Gujarat	85537.63
7	Maharashtra	54003.95
8	West Bengal	32703.86
9	Uttarakhand	25995.57
10	Himachal Pradesh	22120.16
11	Karnataka	9715.78
12	Chhattisgarh	4438.92
13	Jharkhand	3922.80
14	Assam	3854.11
15	Orissa	2614.74
16	Telangana	427.02
17	Andhra Pradesh	60.46
18	Tamil Nadu	4.68
19	Kerala	0.00

wheat_state_d=wheat_state.head(10)
fig=px.bar(wheat_state_d,x="State Name",y="WHEAT PRODUCTION (1000 tons)",
          title="STATE AND WHEAT PRODUCTION (1996-2017)",
          labels={"State Name":"State Name","WHEAT PRODUCTION (1000 tons)":"WHEAT PRODUCTION"},
           color="State Name",
           height=500,
           width=600
          )
fig.update_layout(showlegend=False, title_x=0.5,template="simple_white")

fig.show()

Uttar Pradesh leads in wheat production, producing over 900,000 tons, making it the top wheat-producing state from 1996–2017. Punjab and Haryana follow, with significant but lower production levels than Uttar pradesh. States like Madhya Pradesh, Rajasthan, and Bihar maintain moderate production levels

TOP 10 UTTAR PRADESH DISTRICT WITH WHEAT PRODUCTION

wheat_dist_up=df[df['State Name']== 'Uttar Pradesh']

wheat_dist_up_data=wheat_dist_up.groupby('Dist Name',as_index=False)['WHEAT PRODUCTION (1000 tons)'].sum().sort_values(by='WHEAT PRODUCTION (1000 tons)',ascending=False).reset_index(drop='index')

wheat_dist_up_data

	Dist Name	WHEAT PRODUCTION (1000 tons)
0	Meerut	36102.24
1	Aligarh	35796.82
2	Moradabad	33259.35
3	Buland Shahar	33183.76
4	Basti	32574.10
5	Gorakhpur	32247.82
6	Hardoi	30971.49
7	Budaun	30110.98
8	Shahjahanpur	28319.10
9	Azamgarh	28012.58
10	Kanpur	25543.50
11	Deoria	25511.45
12	Mathura	24417.87
13	Mainpuri	24173.41
14	Etah	24026.80
15	Gonda	23177.16
16	Allahabad	22318.24
17	Faizabad	21294.55
18	Etawah	20816.39
19	Agra	20678.08
20	Kheri	20419.98
21	Saharanpur	20338.19
22	Muzaffarnagar	20074.58
23	Jhansi	19965.01
24	Unnao	19715.17
25	Bahraich	19532.08
26	Bareilly	19467.52
27	Sitapur	19410.13
28	Jaunpur	18686.19
29	Farrukhabad	18662.97
30	Varanasi	18087.57
31	Barabanki	16924.61
32	Pilibhit	16740.14
33	Rampur	16360.55
34	Sultanpur	15444.59
35	Rae-Bareily	15276.75
36	Fatehpur	14605.23
37	Ghazipur	14235.25
38	Bijnor	13520.87
39	Jalaun	13003.51
40	Ballia	12344.54
41	Banda	12338.25
42	Hamirpur	12330.22
43	Pratapgarh	12022.20
44	Mirzpur	10217.39
45	Lucknow	7950.89

wheat_dist_up_data=wheat_dist_up_data.head(10)
fig=px.bar(wheat_dist_up_data,x="Dist Name",y="WHEAT PRODUCTION (1000 tons)",
          title="UP DISTRICT AND RICE PRODUCTION (1996-2017)",
          labels={"Dist Name":"Distrcit","WHEAT PRODUCTION (1000 tons)":"WHEAT PRODUCTION"},
           color="Dist Name",
           height=500,
           width=600
          )
fig.update_layout(showlegend=False, title_x=0.5,template="simple_white")

fig.show()

IN UTTAR PRADESH Meerut HOLDS HIGHEST WHEAT PRODUCTION RECORDS 36K TONS ,FOLLOWED BY ALIGHAR 35K TONS AND MORADABAD 33K TONS

YEAR WISE WHEAT PRODUCTION IN STATES

df_filtered_W=df[['State Name','Year','WHEAT PRODUCTION (1000 tons)']]

df_filtered_data_W=df_filtered_W.groupby(['State Name','Year'],as_index=False)['WHEAT PRODUCTION (1000 tons)'].sum().sort_values(by=['State Name','Year'],ascending=False).reset_index(drop='index')

df_filtered_data_W

	State Name	Year	WHEAT PRODUCTION (1000 tons)
0	West Bengal	2017	359.13
1	West Bengal	2016	858.53
2	West Bengal	2015	788.49
3	West Bengal	2014	939.26
4	West Bengal	2013	927.86
...	...	...	...
1033	Andhra Pradesh	1970	2.30
1034	Andhra Pradesh	1969	0.90
1035	Andhra Pradesh	1968	1.00
1036	Andhra Pradesh	1967	0.50
1037	Andhra Pradesh	1966	0.60

1038 rows × 3 columns

unique_state=df_filtered_data_W['State Name'].unique()[:10]
filtered_w=df_filtered_data_W[df_filtered_data_W['State Name'].isin(unique_state)]
filtered_w

	State Name	Year	WHEAT PRODUCTION (1000 tons)
0	West Bengal	2017	359.13
1	West Bengal	2016	858.53
2	West Bengal	2015	788.49
3	West Bengal	2014	939.26
4	West Bengal	2013	927.86
...	...	...	...
515	Madhya Pradesh	1970	2534.80
516	Madhya Pradesh	1969	2250.20
517	Madhya Pradesh	1968	1953.80
518	Madhya Pradesh	1967	1835.10
519	Madhya Pradesh	1966	1001.40

520 rows × 3 columns

fig=px.line(
    
    filtered_w,
    x='Year',
    y='WHEAT PRODUCTION (1000 tons)',
    title='WHEAT PRODUCTION PER YEAR',
    markers=True,
    color='State Name',
    labels={'Year':'Year','WHEAT PRODUCTION (1000 tons)':'WHEAT PRODUCTION'},
    height=800,
    width=1100
    
)

fig.update_layout(
    template='plotly_white'
)

fig.show()

The graph shows that Uttar Pradesh is the leading wheat producer, followed by Punjab and Madhya Pradesh, with significant growth in production over the years. States like Tamil Nadu, Orissa, and Telangana remain low contributors.

STATE AND WHEAT YIELD

wheat_state_yield=df.groupby('State Name',as_index=False)['WHEAT YIELD (Kg per ha)'].sum().sort_values(by='WHEAT YIELD (Kg per ha)',ascending=False).reset_index(drop='index')

wheat_state_yield=wheat_state_yield.head(10)
wheat_state_yield

	State Name	WHEAT YIELD (Kg per ha)
0	Uttar Pradesh	5208756.43
1	Madhya Pradesh	2930771.89
2	Rajasthan	2722217.00
3	Punjab	2007153.14
4	Gujarat	1991362.26
5	West Bengal	1438980.99
6	Maharashtra	1288086.69
7	Haryana	1161151.95
8	Orissa	1054791.18
9	Bihar	992099.68

fig=px.bar(wheat_state_yield,x="State Name",y="WHEAT YIELD (Kg per ha)",
          title="STATE AND WHEAT YIELD(1996-2017)",
          labels={"State Name":"State","WHEAT YIELD (Kg per ha)":"WHEAT YIELD"},
           color="WHEAT YIELD (Kg per ha)",
           color_continuous_scale='viridis',
           height=500,
           width=600
          )
fig.update_layout( title_x=0.5,template="simple_white")
fig.update_coloraxes(showscale=False)  # Hides the color bar


fig.show()

Uttar Pradesh leads in wheat yield, producing over 5M tons, making it the top wheat-yielding state from 1996–2017. Madhya Pradesh and Rajasthan follow, with significant .

PRODUCTION VS YIELD

wheat_state_new=wheat_state
wheat_state_new

	State Name	WHEAT PRODUCTION (1000 tons)
0	Uttar Pradesh	970210.07
1	Punjab	593848.90
2	Haryana	348429.60
3	Madhya Pradesh	338644.25
4	Rajasthan	265994.99
5	Bihar	176250.30
6	Gujarat	85537.63
7	Maharashtra	54003.95
8	West Bengal	32703.86
9	Uttarakhand	25995.57
10	Himachal Pradesh	22120.16
11	Karnataka	9715.78
12	Chhattisgarh	4438.92
13	Jharkhand	3922.80
14	Assam	3854.11
15	Orissa	2614.74
16	Telangana	427.02
17	Andhra Pradesh	60.46
18	Tamil Nadu	4.68
19	Kerala	0.00

marge_table_w=pd.merge(wheat_state_yield,wheat_state_new,on='State Name',how='left')
marge_table_w.style.background_gradient(cmap='spring')

	State Name	WHEAT YIELD (Kg per ha)	WHEAT PRODUCTION (1000 tons)
0	Uttar Pradesh	5208756.430000	970210.070000
1	Madhya Pradesh	2930771.890000	338644.250000
2	Rajasthan	2722217.000000	265994.990000
3	Punjab	2007153.140000	593848.900000
4	Gujarat	1991362.260000	85537.630000
5	West Bengal	1438980.990000	32703.860000
6	Maharashtra	1288086.690000	54003.950000
7	Haryana	1161151.950000	348429.600000
8	Orissa	1054791.180000	2614.740000
9	Bihar	992099.680000	176250.300000

marge_table_data_w=marge_table_w.melt(id_vars='State Name',var_name='Metric',value_name='Count')
marge_table_data_w

	State Name	Metric	Count
0	Uttar Pradesh	WHEAT YIELD (Kg per ha)	5208756.43
1	Madhya Pradesh	WHEAT YIELD (Kg per ha)	2930771.89
2	Rajasthan	WHEAT YIELD (Kg per ha)	2722217.00
3	Punjab	WHEAT YIELD (Kg per ha)	2007153.14
4	Gujarat	WHEAT YIELD (Kg per ha)	1991362.26
5	West Bengal	WHEAT YIELD (Kg per ha)	1438980.99
6	Maharashtra	WHEAT YIELD (Kg per ha)	1288086.69
7	Haryana	WHEAT YIELD (Kg per ha)	1161151.95
8	Orissa	WHEAT YIELD (Kg per ha)	1054791.18
9	Bihar	WHEAT YIELD (Kg per ha)	992099.68
10	Uttar Pradesh	WHEAT PRODUCTION (1000 tons)	970210.07
11	Madhya Pradesh	WHEAT PRODUCTION (1000 tons)	338644.25
12	Rajasthan	WHEAT PRODUCTION (1000 tons)	265994.99
13	Punjab	WHEAT PRODUCTION (1000 tons)	593848.90
14	Gujarat	WHEAT PRODUCTION (1000 tons)	85537.63
15	West Bengal	WHEAT PRODUCTION (1000 tons)	32703.86
16	Maharashtra	WHEAT PRODUCTION (1000 tons)	54003.95
17	Haryana	WHEAT PRODUCTION (1000 tons)	348429.60
18	Orissa	WHEAT PRODUCTION (1000 tons)	2614.74
19	Bihar	WHEAT PRODUCTION (1000 tons)	176250.30

fig=px.bar(
    marge_table_data_w,
    x='State Name',
    y='Count',
    color='Metric',
    barmode='group',
     width=900,
    height=400,
    color_discrete_sequence=['#482ff7', '#21e6c1']
)
fig.update_layout(title="PRODUCTION VS YIELD",
                  title_x=0.2
)

fig.show()

The graph shows that Uttar Pradesh dominates in both wheat production and yield, followed by Madhya Pradesh and Rajasthan in production. States like Punjab and Haryana have relatively high yields but moderate production. Orissa and Bihar have the lowest wheat production and yield among the states. Overall, the data highlights Uttar Pradesh's leading role in wheat cultivation, with variations in productivity across states

-------- FROM THE ABOVE WE CAN ETRACT INSIGHT FROM DIFFERNT CROP BUT IT COULD BORING SO TRY SOME NEW EXPERIMENT ----------

Dominant Crop Analysis: Identify the dominant crops of WEST BENGAL.

wb=df[df['State Name']=='West Bengal']

wb

	Dist Code	Year	State Code	State Name	Dist Name	RICE AREA (1000 ha)	RICE PRODUCTION (1000 tons)	RICE YIELD (Kg per ha)	WHEAT AREA (1000 ha)	WHEAT PRODUCTION (1000 tons)	...	SUGARCANE YIELD (Kg per ha)	COTTON AREA (1000 ha)	COTTON PRODUCTION (1000 tons)	COTTON YIELD (Kg per ha)	FRUITS AREA (1000 ha)	VEGETABLES AREA (1000 ha)	FRUITS AND VEGETABLES AREA (1000 ha)	POTATOES AREA (1000 ha)	ONION AREA (1000 ha)	FODDER AREA (1000 ha)
14500	800	1966	13	West Bengal	24 Parganas	603.36	628.09	1040.99	1.16	1.04	...	4200.00	0.0	0.0	0.0	5.26	26.63	31.89	3.20	-1.0	-1.0
14501	800	1967	13	West Bengal	24 Parganas	618.82	682.16	1102.36	2.29	1.98	...	3815.60	0.0	0.0	0.0	5.26	26.95	32.21	3.52	-1.0	-1.0
14502	800	1968	13	West Bengal	24 Parganas	615.70	703.49	1142.59	3.92	4.86	...	5021.16	0.0	0.0	0.0	29.02	28.61	57.63	3.00	-1.0	-1.0
14503	800	1969	13	West Bengal	24 Parganas	633.20	672.19	1061.58	5.43	6.19	...	5201.92	0.0	0.0	0.0	29.06	30.84	59.89	2.79	-1.0	-1.0
14504	800	1970	13	West Bengal	24 Parganas	582.40	475.70	816.79	9.09	11.27	...	4418.48	0.0	0.0	0.0	28.05	45.24	73.29	2.23	-1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
15269	822	2013	13	West Bengal	West Dinajpur	407.78	1069.76	2623.38	50.05	134.32	...	13000.00	0.0	0.0	0.0	14.41	95.38	109.79	14.76	0.0	0.0
15270	822	2014	13	West Bengal	West Dinajpur	399.61	1120.63	2804.31	51.62	131.02	...	10581.40	0.0	0.0	0.0	14.50	94.05	108.55	13.28	0.0	0.0
15271	822	2015	13	West Bengal	West Dinajpur	414.94	1195.03	2880.01	62.70	161.25	...	9366.67	0.0	0.0	0.0	14.92	95.77	110.70	14.86	0.0	0.0
15272	822	2016	13	West Bengal	West Dinajpur	402.75	1141.76	2834.93	58.24	149.62	...	8441.01	0.0	0.0	0.0	15.53	95.91	111.43	14.65	0.0	0.0
15273	822	2017	13	West Bengal	West Dinajpur	417.97	1082.16	2589.08	44.44	149.23	...	9167.84	0.0	0.0	0.0	16.08	97.69	113.77	16.56	0.0	0.0

774 rows × 80 columns

production_column=[col for col in wb.columns if 'PRODUCTION' in col]

district_column=wb.groupby('Dist Name',as_index=False)[production_column].sum().sort_values(by=production_column,ascending=False)

district_column

	Dist Name	RICE PRODUCTION (1000 tons)	WHEAT PRODUCTION (1000 tons)	KHARIF SORGHUM PRODUCTION (1000 tons)	RABI SORGHUM PRODUCTION (1000 tons)	SORGHUM PRODUCTION (1000 tons)	PEARL MILLET PRODUCTION (1000 tons)	MAIZE PRODUCTION (1000 tons)	FINGER MILLET PRODUCTION (1000 tons)	BARLEY PRODUCTION (1000 tons)	...	SESAMUM PRODUCTION (1000 tons)	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	SAFFLOWER PRODUCTION (1000 tons)	CASTOR PRODUCTION (1000 tons)	LINSEED PRODUCTION (1000 tons)	SUNFLOWER PRODUCTION (1000 tons)	SOYABEAN PRODUCTION (1000 tons)	OILSEEDS PRODUCTION (1000 tons)	SUGARCANE PRODUCTION (1000 tons)	COTTON PRODUCTION (1000 tons)
10	Midnapur	98868.28	747.93	0.22	0.00	0.24	0.03	90.57	0.12	0.19	...	1211.28	573.24	1.81	1.27	0.11	13.80	0.19	2361.16	1142.53	3.05
3	Burdwan	71422.15	694.80	0.00	0.00	0.00	0.44	20.17	0.00	4.94	...	283.61	1142.19	0.17	0.00	0.77	1.69	0.24	1292.76	433.04	1.21
0	24 Parganas	63011.99	1068.05	0.00	0.00	0.00	0.00	23.47	0.00	1.17	...	288.19	1363.37	1.34	0.00	1.20	91.43	1.42	1458.49	297.95	9.57
2	Birbhum	42213.81	2815.82	1.74	0.00	2.10	0.71	27.29	0.00	15.95	...	62.76	1092.26	0.25	0.00	1.89	0.76	2.85	950.54	530.78	0.24
1	Bankura	40165.20	738.44	0.62	0.00	0.68	0.86	75.16	0.04	2.36	...	408.54	358.79	0.00	0.00	0.62	7.27	3.79	692.27	176.26	0.24
11	Murshidabad	37261.24	10582.36	5.48	0.00	5.62	0.53	418.09	0.00	325.77	...	285.78	2399.09	0.04	0.00	6.70	1.19	0.01	2071.04	1675.65	1.72
14	West Dinajpur	35224.87	3679.53	0.00	0.02	0.02	0.00	3174.87	15.99	77.49	...	34.25	1630.82	0.15	0.00	11.69	0.73	0.14	1160.18	184.29	0.13
6	Hooghly	30962.21	311.67	0.03	0.00	0.03	0.09	7.71	0.00	0.30	...	669.14	310.91	0.00	0.00	0.00	1.20	0.59	1120.75	110.06	0.35
12	Nadia	28016.30	4951.66	0.06	0.00	0.06	0.00	160.88	0.00	53.34	...	543.62	2254.79	0.42	0.00	15.52	9.07	0.11	2534.10	932.38	0.53
13	Purulia	23147.50	191.27	0.61	0.00	0.68	0.41	568.22	2.44	0.09	...	13.81	39.48	0.30	0.00	4.97	0.60	0.22	104.31	158.09	0.36
9	Malda	22497.83	4163.46	0.00	0.00	0.00	0.00	1077.60	0.00	318.44	...	4.65	1034.48	0.00	0.00	4.34	0.74	1.30	827.82	954.92	0.20
4	Cooch Behar	20949.15	1260.54	0.00	0.00	0.00	0.12	1044.98	0.26	15.20	...	13.46	232.15	0.00	0.00	9.60	0.20	0.15	259.89	41.29	0.49
8	Jalpaiguri	17372.00	1275.53	0.00	0.00	0.00	0.00	772.30	8.07	22.73	...	25.76	205.60	0.57	0.00	7.46	1.29	0.03	272.12	39.49	0.33
7	Howrah	10264.53	44.05	0.00	0.00	0.00	0.00	2.31	0.00	0.05	...	62.70	28.83	0.00	0.00	0.01	1.87	0.00	205.40	24.77	0.93
5	Darjeeling	2855.20	178.75	0.00	0.00	0.00	0.00	2424.91	329.32	10.11	...	3.67	11.39	0.01	0.00	1.21	0.15	8.08	12.74	4.66	0.07

15 rows × 24 columns

district_column_n=wb.groupby('State Name',as_index=False)[production_column].sum().sort_values(by=production_column,ascending=False)

district_column_n

	State Name	RICE PRODUCTION (1000 tons)	WHEAT PRODUCTION (1000 tons)	KHARIF SORGHUM PRODUCTION (1000 tons)	RABI SORGHUM PRODUCTION (1000 tons)	SORGHUM PRODUCTION (1000 tons)	PEARL MILLET PRODUCTION (1000 tons)	MAIZE PRODUCTION (1000 tons)	FINGER MILLET PRODUCTION (1000 tons)	BARLEY PRODUCTION (1000 tons)	...	SESAMUM PRODUCTION (1000 tons)	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	SAFFLOWER PRODUCTION (1000 tons)	CASTOR PRODUCTION (1000 tons)	LINSEED PRODUCTION (1000 tons)	SUNFLOWER PRODUCTION (1000 tons)	SOYABEAN PRODUCTION (1000 tons)	OILSEEDS PRODUCTION (1000 tons)	SUGARCANE PRODUCTION (1000 tons)	COTTON PRODUCTION (1000 tons)
0	West Bengal	544232.26	32703.86	8.76	0.02	9.43	3.19	9888.53	356.24	848.13	...	3911.22	12677.39	5.06	1.27	66.09	131.99	19.12	15323.57	6706.16	19.42

1 rows × 24 columns

melted_df = district_column_n.melt(id_vars="State Name", var_name="Crop", value_name="Production (1000 tons)")

# Sort the melted DataFrame by 'Production (1000 tons)' in descending order and select the top 10
top_10_crops = melted_df.sort_values('Production (1000 tons)', ascending=False).reset_index(drop='index').head(10)

top_10_crops.drop('State Name',axis=1).style.background_gradient(cmap='winter')

	Crop	Production (1000 tons)
0	RICE PRODUCTION (1000 tons)	544232.260000
1	WHEAT PRODUCTION (1000 tons)	32703.860000
2	OILSEEDS PRODUCTION (1000 tons)	15323.570000
3	MINOR PULSES PRODUCTION (1000 tons)	13188.910000
4	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	12677.390000
5	MAIZE PRODUCTION (1000 tons)	9888.530000
6	SUGARCANE PRODUCTION (1000 tons)	6706.160000
7	SESAMUM PRODUCTION (1000 tons)	3911.220000
8	GROUNDNUT PRODUCTION (1000 tons)	2442.220000
9	CHICKPEA PRODUCTION (1000 tons)	2103.980000

results = []

# Iterate over production columns to find max values
for col in district_column.columns[1:]:  # Skip 'Dist Name'
    max_value = district_column[col].max()
    dist_name = district_column[district_column[col] == max_value]['Dist Name'].values[0]
    results.append({'Metric': col, 'Max Value': max_value, 'District Name': dist_name})

results_df = pd.DataFrame(results)

results_df.style.background_gradient(cmap='plasma')

	Metric	Max Value	District Name
0	RICE PRODUCTION (1000 tons)	98868.280000	Midnapur
1	WHEAT PRODUCTION (1000 tons)	10582.360000	Murshidabad
2	KHARIF SORGHUM PRODUCTION (1000 tons)	5.480000	Murshidabad
3	RABI SORGHUM PRODUCTION (1000 tons)	0.020000	West Dinajpur
4	SORGHUM PRODUCTION (1000 tons)	5.620000	Murshidabad
5	PEARL MILLET PRODUCTION (1000 tons)	0.860000	Bankura
6	MAIZE PRODUCTION (1000 tons)	3174.870000	West Dinajpur
7	FINGER MILLET PRODUCTION (1000 tons)	329.320000	Darjeeling
8	BARLEY PRODUCTION (1000 tons)	325.770000	Murshidabad
9	CHICKPEA PRODUCTION (1000 tons)	733.400000	Nadia
10	PIGEONPEA PRODUCTION (1000 tons)	34.940000	Murshidabad
11	MINOR PULSES PRODUCTION (1000 tons)	3308.870000	Murshidabad
12	GROUNDNUT PRODUCTION (1000 tons)	1192.480000	Midnapur
13	SESAMUM PRODUCTION (1000 tons)	1211.280000	Midnapur
14	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	2399.090000	Murshidabad
15	SAFFLOWER PRODUCTION (1000 tons)	1.810000	Midnapur
16	CASTOR PRODUCTION (1000 tons)	1.270000	Midnapur
17	LINSEED PRODUCTION (1000 tons)	15.520000	Nadia
18	SUNFLOWER PRODUCTION (1000 tons)	91.430000	24 Parganas
19	SOYABEAN PRODUCTION (1000 tons)	8.080000	Darjeeling
20	OILSEEDS PRODUCTION (1000 tons)	2534.100000	Nadia
21	SUGARCANE PRODUCTION (1000 tons)	1675.650000	Murshidabad
22	COTTON PRODUCTION (1000 tons)	9.570000	24 Parganas

MOST PRODUCE CROP IN INDIA

production_columns=[col for col in df.columns if 'PRODUCTION' in col]

production_data=df.groupby('State Name',as_index=False)[production_column].sum().sort_values(by=production_column,ascending=False)

result_data=[]
for col in production_data.columns[1:]:
    max_value=production_data[col].max()
    state_name=production_data[production_data[col]==max_value]['State Name'].values[0]
    result_data.append({'Metric':col,'Max Value':max_value,'State Name':state_name})

result_data=pd.DataFrame(result_data)

result_data

	Metric	Max Value	State Name
0	RICE PRODUCTION (1000 tons)	544232.26	West Bengal
1	WHEAT PRODUCTION (1000 tons)	970210.07	Uttar Pradesh
2	KHARIF SORGHUM PRODUCTION (1000 tons)	116655.48	Maharashtra
3	RABI SORGHUM PRODUCTION (1000 tons)	85454.84	Maharashtra
4	SORGHUM PRODUCTION (1000 tons)	202055.20	Maharashtra
5	PEARL MILLET PRODUCTION (1000 tons)	118645.76	Rajasthan
6	MAIZE PRODUCTION (1000 tons)	75586.69	Karnataka
7	FINGER MILLET PRODUCTION (1000 tons)	63473.92	Karnataka
8	BARLEY PRODUCTION (1000 tons)	39496.06	Uttar Pradesh
9	CHICKPEA PRODUCTION (1000 tons)	97533.44	Madhya Pradesh
10	PIGEONPEA PRODUCTION (1000 tons)	31084.27	Maharashtra
11	MINOR PULSES PRODUCTION (1000 tons)	36509.70	Orissa
12	GROUNDNUT PRODUCTION (1000 tons)	98321.42	Gujarat
13	SESAMUM PRODUCTION (1000 tons)	4941.67	Orissa
14	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	86067.89	Rajasthan
15	SAFFLOWER PRODUCTION (1000 tons)	8151.93	Maharashtra
16	CASTOR PRODUCTION (1000 tons)	26764.52	Gujarat
17	LINSEED PRODUCTION (1000 tons)	4513.49	Madhya Pradesh
18	SUNFLOWER PRODUCTION (1000 tons)	10785.87	Karnataka
19	SOYABEAN PRODUCTION (1000 tons)	136006.52	Madhya Pradesh
20	OILSEEDS PRODUCTION (1000 tons)	153594.79	Madhya Pradesh
21	SUGARCANE PRODUCTION (1000 tons)	506242.87	Uttar Pradesh
22	COTTON PRODUCTION (1000 tons)	31359.57	Gujarat

fig = px.scatter(
    result_data,
    x="State Name",
    y="Max Value",
    size="Max Value",
    color="Metric",
    hover_name="State Name",
    title=" Production Values by State",
    labels={"Max Value": "Production (1000 tons)", "State Name": "State"},
    height=600,
    width=1100,
        size_max=60  # Increase this value for larger circles

)
fig.update_layout(showlegend=False)
fig.show()

Dominant States: Uttar Pradesh and West Bengal lead in production, with Uttar Pradesh having the highest value across all states.

Moderate Contributions: Maharashtra, Madhya Pradesh, and Karnataka contribute moderately, showing smaller production volumes.

Relatively Low Production: States like Orissa and Gujarat have the smallest production values compared to others.

Crop Production Over Year

data_column=df.groupby('Year',as_index=False)[production_column].sum().sort_values(by='Year')

data_column

	Year	RICE PRODUCTION (1000 tons)	WHEAT PRODUCTION (1000 tons)	KHARIF SORGHUM PRODUCTION (1000 tons)	RABI SORGHUM PRODUCTION (1000 tons)	SORGHUM PRODUCTION (1000 tons)	PEARL MILLET PRODUCTION (1000 tons)	MAIZE PRODUCTION (1000 tons)	FINGER MILLET PRODUCTION (1000 tons)	BARLEY PRODUCTION (1000 tons)	...	SESAMUM PRODUCTION (1000 tons)	RAPESEED AND MUSTARD PRODUCTION (1000 tons)	SAFFLOWER PRODUCTION (1000 tons)	CASTOR PRODUCTION (1000 tons)	LINSEED PRODUCTION (1000 tons)	SUNFLOWER PRODUCTION (1000 tons)	SOYABEAN PRODUCTION (1000 tons)	OILSEEDS PRODUCTION (1000 tons)	SUGARCANE PRODUCTION (1000 tons)	COTTON PRODUCTION (1000 tons)
0	1966	29133.23	10989.12	5486.70	3364.00	8860.70	4540.06	4832.23	1689.41	2293.08	...	352.73	437.43	-14.63	84.26	128.46	-9.48	-10.00	3666.49	9361.71	862.44
1	1967	35056.64	16006.30	6694.08	3288.30	9992.38	5246.99	6087.13	1726.90	3392.73	...	372.63	563.77	-10.78	96.76	204.54	-9.98	-10.00	4232.25	9574.38	953.06
2	1968	34701.52	17375.67	6080.97	3222.00	9312.97	3853.45	4485.59	1528.89	2261.35	...	313.31	442.99	-8.00	91.91	172.11	-9.94	-10.00	3835.23	12720.70	930.25
3	1969	35836.01	19232.36	6039.11	2930.90	8980.01	5516.42	4781.64	1659.85	2600.63	...	339.80	536.73	-17.15	97.43	199.50	-9.32	-10.00	5879.42	13719.57	937.48
4	1970	39324.12	22677.88	5816.05	2268.60	8084.65	8018.16	7125.88	1879.32	2753.04	...	449.21	762.19	3.88	129.52	223.91	0.28	2.16	5530.06	12381.60	860.57
5	1971	39946.79	25280.89	5349.11	2401.40	7747.21	5250.84	4991.21	2006.80	2500.39	...	382.82	660.50	15.56	138.82	273.67	0.37	0.00	7613.19	11664.01	1295.85
6	1972	37925.93	22879.56	5245.39	1508.00	6753.39	3935.34	5831.52	1695.74	2281.57	...	285.13	853.68	94.35	120.03	229.34	0.05	0.02	5209.08	12573.28	1053.83
7	1973	42026.94	20833.39	5898.06	3117.30	9015.36	7266.05	5300.12	1879.76	2285.23	...	368.66	763.88	167.48	213.11	316.52	1.16	1.09	7585.19	14392.62	1122.84
8	1974	37690.51	23052.96	7020.37	3183.40	10203.77	3388.46	5117.28	2027.00	3030.12	...	305.65	1083.97	167.31	209.09	388.75	0.03	0.00	6806.88	14640.45	1236.49
9	1975	46144.36	27150.41	6965.04	2497.20	9462.24	5625.08	6515.49	2652.48	3107.80	...	366.91	821.57	178.50	117.40	346.17	0.66	6.50	5909.22	14931.26	1077.49
10	1976	40226.45	27580.73	7300.23	3080.90	10381.13	5631.29	5775.28	1981.01	2291.18	...	321.02	595.70	185.67	181.26	257.61	0.04	0.01	6730.09	15480.12	1074.44
11	1977	50439.41	30003.66	8573.67	3110.00	11683.67	4722.99	5490.49	3000.37	2248.46	...	377.26	814.20	157.05	217.11	353.43	0.12	0.11	7741.30	18136.92	1254.38
12	1978	50868.02	34078.29	8165.40	3385.60	11551.00	5510.36	5667.17	3123.91	2059.29	...	404.21	872.99	185.85	210.56	310.50	0.09	2.67	7836.97	15610.47	1325.75
13	1979	40560.64	30742.00	7828.16	3854.30	11682.46	4535.74	5379.91	2716.87	1610.68	...	288.93	693.06	200.77	208.57	148.31	5.21	2.78	7754.69	12689.56	1278.65
14	1980	52796.25	35659.06	7772.94	2865.40	10638.34	5363.72	6248.26	2445.12	2291.28	...	346.66	1140.37	309.56	184.00	210.30	67.22	0.01	7271.75	15153.07	1181.58
15	1981	52486.37	37154.29	8564.35	3095.30	11659.65	5360.47	6291.93	2873.61	1984.59	...	388.75	1619.77	393.30	278.06	233.71	129.18	0.46	10232.67	18017.94	1340.80
16	1982	45081.34	42402.18	7632.60	3091.00	10723.60	5110.52	6115.98	2573.18	1834.85	...	381.07	1564.50	342.10	323.82	238.68	223.12	478.37	8721.27	18617.78	1266.06
17	1983	57766.50	44859.22	8637.83	3279.00	11916.83	7603.26	7471.18	2978.23	1777.70	...	404.88	1969.33	449.78	380.68	317.41	270.32	573.73	11514.37	17344.32	1072.95
18	1984	56451.83	43846.97	7775.80	3461.70	11237.50	6151.85	7758.14	2417.34	1521.29	...	454.76	2546.05	469.52	439.14	260.33	363.20	923.00	11795.38	17009.60	1428.66
19	1985	62289.85	46811.09	7452.37	2650.30	10102.67	3664.26	6183.83	2483.44	1934.94	...	428.37	2242.26	292.01	281.24	251.70	300.04	970.90	9921.94	16843.80	1477.05
20	1986	58592.01	43499.94	6471.93	2427.20	8899.13	4461.36	6664.06	2637.91	1650.74	...	358.51	2224.27	304.10	215.93	234.39	434.30	826.99	10152.00	17952.70	1195.61
21	1987	54890.34	45504.65	8624.97	3382.40	12030.77	3355.98	5263.26	2283.46	1558.97	...	406.18	2803.66	422.97	161.84	261.69	675.00	866.59	11686.28	19262.10	1105.92
22	1988	68923.09	53682.89	7336.61	3231.90	10568.84	7671.55	7717.07	2370.99	1702.71	...	632.81	3846.54	414.64	387.20	237.26	429.50	1490.36	17248.55	20234.53	1474.57
23	1989	73530.86	52123.31	9345.34	3654.52	13007.26	6730.73	9045.98	2731.76	1477.92	...	698.08	3618.10	461.56	477.70	226.50	323.98	1690.51	16330.07	22000.56	1945.94
24	1990	71613.37	54449.14	8247.02	3271.12	11582.98	6998.45	8319.23	2080.87	1631.06	...	814.47	4552.98	326.91	828.54	293.20	849.08	2575.99	17864.14	24073.07	1729.39
25	1991	71858.31	55378.51	5712.65	2326.94	8001.14	4665.81	7409.00	2353.91	1695.24	...	674.41	5197.85	212.40	686.80	256.16	887.67	2457.90	17662.19	25666.70	1683.73
26	1992	70205.91	56665.30	9422.41	3429.60	12852.02	9082.92	9448.88	2487.13	1506.38	...	840.06	4276.52	356.53	686.77	248.35	971.77	3364.93	20033.99	23642.98	1955.51
27	1993	77179.69	59400.67	7304.85	4116.84	11421.67	5197.32	8922.89	2592.15	1322.21	...	683.40	4752.64	522.48	720.83	295.84	1238.55	4722.13	21311.61	24187.27	1903.01
28	1994	79273.91	65462.30	5765.74	3001.03	9018.11	7215.85	8394.53	2250.94	1691.17	...	723.57	5262.02	422.66	157.45	299.53	1065.46	3899.72	21145.20	28496.59	1606.59
29	1995	72615.41	61157.13	5854.91	3602.88	9725.29	5477.49	8737.49	2393.76	1493.37	...	692.12	5462.46	373.99	859.32	235.60	1199.66	5069.48	22163.81	29164.69	2200.37
30	1996	78120.14	68631.56	6971.92	3971.08	10952.98	7872.34	9836.78	2154.45	1444.89	...	687.50	6189.70	443.92	897.49	276.60	1183.80	5348.47	24112.65	27444.29	2420.62
31	1997	80504.09	65085.19	5105.31	2542.60	7727.60	7719.64	10375.90	1960.93	1693.80	...	626.29	4210.55	120.77	1001.59	214.32	739.10	6395.28	21090.35	27612.42	1855.14
32	1998	82394.23	70057.41	5366.80	3111.04	8616.86	7097.60	10325.04	2471.65	1425.28	...	577.99	5096.13	230.92	993.94	251.44	818.36	6941.60	24400.08	29533.07	2102.13
33	1999	86527.07	76209.47	4965.31	3750.63	8788.53	5900.35	10768.78	2376.65	1412.58	...	536.30	5290.06	259.71	765.06	249.69	672.33	7055.96	19335.19	30489.42	1950.34
34	2000	83476.59	69617.04	4617.47	2938.68	7556.21	6903.59	11665.88	2719.25	1424.68	...	545.65	4123.99	201.66	844.47	164.58	626.82	5239.11	17998.41	30147.27	1628.93
35	2001	91556.12	72186.10	4287.29	3286.77	7573.95	8509.41	12339.00	2430.45	1420.48	...	739.96	4734.67	220.18	826.90	181.65	673.78	5896.75	20573.98	30095.87	1676.30
36	2002	69917.86	65404.66	4324.62	2750.30	7067.04	4953.81	10483.11	1399.26	1377.04	...	449.21	3479.19	179.15	578.47	158.19	856.84	4542.82	14612.60	28905.09	1496.10
37	2003	84971.15	72317.08	4865.17	1876.08	6641.16	12165.07	13949.32	1959.68	1300.09	...	825.92	5971.14	136.34	712.51	163.37	913.62	7634.82	25543.15	23810.49	2331.39
38	2004	80951.64	69013.36	4021.07	3037.93	7050.69	7925.63	13125.05	2398.41	1149.85	...	739.83	7007.32	179.54	804.78	139.84	1125.80	6682.77	24653.46	24070.93	2907.04
39	2005	87315.59	68379.02	3876.97	3343.07	7219.76	7148.42	14553.86	2413.40	1195.60	...	748.60	7943.48	230.01	945.24	150.08	1299.10	8514.67	28187.86	28225.44	3149.82
40	2006	89984.14	77709.50	3731.29	3226.78	6958.60	8382.21	14222.14	1398.54	1332.90	...	780.64	7029.37	238.96	809.49	138.39	1069.00	8761.05	24825.53	33956.80	3674.08
41	2007	93002.77	80126.73	4133.71	3608.74	7749.29	10060.64	18908.40	2165.25	1227.92	...	899.08	5407.15	225.87	1059.40	129.66	1309.23	10781.17	29765.84	35457.94	4383.78
42	2008	95226.84	81613.21	2747.30	3916.36	6880.79	8998.39	18206.51	1981.27	1694.95	...	745.03	6835.33	170.95	1161.53	125.56	1037.67	8952.14	25550.46	28864.83	3786.71
43	2009	88070.69	81022.42	2399.12	3778.32	6576.27	6597.51	16025.69	1889.29	1311.67	...	650.74	6287.93	167.72	1008.08	130.93	763.38	9837.74	23867.87	29682.38	3809.38
44	2010	94300.23	93438.55	3409.57	3568.25	6976.15	12376.91	22120.65	2281.21	1709.20	...	1007.54	7495.54	149.98	1349.78	106.70	639.30	12790.53	32662.73	34099.05	4825.53
45	2011	102216.27	100269.99	3219.25	2672.24	5891.51	12413.46	21864.68	2049.04	1643.72	...	856.81	6374.63	114.36	2315.28	132.66	495.12	12165.17	29444.86	38988.84	5009.24
46	2012	104237.62	97177.86	2885.20	2583.01	5553.00	8820.76	22911.10	1645.51	1810.33	...	804.62	7812.51	96.80	1963.29	121.56	515.54	15235.66	21955.79	35213.44	4200.33
47	2013	109552.42	102506.18	2671.98	3564.29	6264.41	9930.81	26302.00	2137.59	1804.10	...	727.49	7252.89	125.03	1764.21	109.78	476.37	11004.62	34919.91	36270.47	5982.39
48	2014	112551.62	84866.58	2433.30	3305.56	5747.18	9413.04	24732.81	2132.44	1536.47	...	894.94	5940.38	69.69	1853.14	111.49	377.68	9730.87	26733.04	38374.91	4775.65
49	2015	98820.51	93824.98	1697.37	2123.17	3825.01	7805.13	22118.14	1913.92	1429.72	...	883.51	6562.40	37.56	1742.00	84.69	226.29	7586.38	21787.71	33160.24	3789.06
50	2016	117614.10	112962.82	1979.27	2984.57	5002.37	9802.02	27182.66	1095.72	1923.68	...	786.61	7872.89	92.65	1373.79	141.80	228.06	13133.13	4036.67	32224.82	5415.75
51	2017	114319.61	110418.21	2315.84	3476.25	5820.90	9282.15	32725.03	2318.14	1922.12	...	835.00	8315.89	56.32	1720.04	165.00	252.96	11062.44	4546.56	37040.79	5722.10

52 rows × 24 columns

df_melted_data=data_column.melt(id_vars=['Year'],var_name='Crop',value_name="Production (1000 tons)")

df_melted_data

	Year	Crop	Production (1000 tons)
0	1966	RICE PRODUCTION (1000 tons)	29133.23
1	1967	RICE PRODUCTION (1000 tons)	35056.64
2	1968	RICE PRODUCTION (1000 tons)	34701.52
3	1969	RICE PRODUCTION (1000 tons)	35836.01
4	1970	RICE PRODUCTION (1000 tons)	39324.12
...	...	...	...
1191	2013	COTTON PRODUCTION (1000 tons)	5982.39
1192	2014	COTTON PRODUCTION (1000 tons)	4775.65
1193	2015	COTTON PRODUCTION (1000 tons)	3789.06
1194	2016	COTTON PRODUCTION (1000 tons)	5415.75
1195	2017	COTTON PRODUCTION (1000 tons)	5722.10

1196 rows × 3 columns

fig = px.line(df_melted_data, x="Year", y="Production (1000 tons)", color="Crop",
              title="Crop Production Over Years", labels={"Production (1000 tons)": "Production"},
             height=650,
             width=1250)
fig.update_traces(mode='lines+markers')
fig.show()

Rice and wheat dominate crop production, with consistent growth from 1970 to 2020, reflecting advancements in agriculture. Other crops like maize, sugarcane, and cotton show moderate growth, while minor crops exhibit flat or fluctuating trends, highlighting a focus on staple crops over diversification

Areas vs Yield

# Scatter Plot: Rice Area vs. Rice Yield
fig_scatter = px.scatter(df, x='RICE AREA (1000 ha)', y='RICE YIELD (Kg per ha)', 
                         title='Rice Area vs. Rice Yield',
                                          color='State Name',  # Color by state

                         labels={'RICE AREA (1000 ha)': 'Rice Area (1000 ha)', 'RICE YIELD (Kg per ha)': 'Rice Yield (Kg/ha)'})

fig_scatter.show()

correlation = df['RICE AREA (1000 ha)'].corr(df['RICE YIELD (Kg per ha)'])

print(f"Correlation between Rice Area and Rice Yield: {correlation:.2f}")

Correlation between Rice Area and Rice Yield: 0.22

States with high yield and small area may have more efficient farming practices or better infrastructure. one state shows consistently high yields, it can act as a benchmark for others .Increasing the area might not improve yield after a certain threshold, emphasizing the importance of improving yield per hectare.

A correlation coefficient of 0.22 indicates a weak positive relationship between Rice Area and Rice Yield.There is a slight tendency for higher rice cultivation areas to be associated with higher yields, but the relationship is not strong. This suggests that other factors likely have a more significant impact on yield than the size of the area.

Crop Areas Across States

area_column=[col for col in wb.columns if 'AREA' in col]

area_column

['RICE AREA (1000 ha)',
 'WHEAT AREA (1000 ha)',
 'KHARIF SORGHUM AREA (1000 ha)',
 'RABI SORGHUM AREA (1000 ha)',
 'SORGHUM AREA (1000 ha)',
 'PEARL MILLET AREA (1000 ha)',
 'MAIZE AREA (1000 ha)',
 'FINGER MILLET AREA (1000 ha)',
 'BARLEY AREA (1000 ha)',
 'CHICKPEA AREA (1000 ha)',
 'PIGEONPEA AREA (1000 ha)',
 'MINOR PULSES AREA (1000 ha)',
 'GROUNDNUT AREA (1000 ha)',
 'SESAMUM AREA (1000 ha)',
 'RAPESEED AND MUSTARD AREA (1000 ha)',
 'SAFFLOWER AREA (1000 ha)',
 'CASTOR AREA (1000 ha)',
 'LINSEED AREA (1000 ha)',
 'SUNFLOWER AREA (1000 ha)',
 'SOYABEAN AREA (1000 ha)',
 'OILSEEDS AREA (1000 ha)',
 'SUGARCANE AREA (1000 ha)',
 'COTTON AREA (1000 ha)',
 'FRUITS AREA (1000 ha)',
 'VEGETABLES AREA (1000 ha)',
 'FRUITS AND VEGETABLES AREA (1000 ha)',
 'POTATOES AREA (1000 ha)',
 'ONION AREA (1000 ha)',
 'FODDER AREA (1000 ha)']

area_column_data=df.groupby('State Name',as_index=False)[area_column].sum().sort_values(by=area_column,ascending=False)

area_column_data

	State Name	RICE AREA (1000 ha)	WHEAT AREA (1000 ha)	KHARIF SORGHUM AREA (1000 ha)	RABI SORGHUM AREA (1000 ha)	SORGHUM AREA (1000 ha)	PEARL MILLET AREA (1000 ha)	MAIZE AREA (1000 ha)	FINGER MILLET AREA (1000 ha)	BARLEY AREA (1000 ha)	...	SOYABEAN AREA (1000 ha)	OILSEEDS AREA (1000 ha)	SUGARCANE AREA (1000 ha)	COTTON AREA (1000 ha)	FRUITS AREA (1000 ha)	VEGETABLES AREA (1000 ha)	FRUITS AND VEGETABLES AREA (1000 ha)	POTATOES AREA (1000 ha)	ONION AREA (1000 ha)	FODDER AREA (1000 ha)
19	West Bengal	279491.45	15500.29	18.40	0.06	19.75	5.71	3434.61	327.95	1051.47	...	29.61	20300.76	1037.86	51.99	4815.92	27656.60	32505.49	12191.16	483.97	-106.91
17	Uttar Pradesh	269169.06	418455.35	24392.94	0.00	24803.58	47885.77	55127.87	333.72	29135.26	...	819.27	43047.07	93073.41	1090.35	10952.97	25228.66	36272.57	18358.69	1012.88	35534.92
12	Orissa	225285.64	1568.75	978.32	0.00	993.61	255.05	8637.29	10853.31	0.00	...	21.84	39682.05	2018.92	1660.49	12050.33	30205.39	40310.93	621.53	1784.09	-312.00
3	Chhattisgarh	184036.34	5093.80	569.86	28.11	620.68	4.24	5089.80	588.21	331.03	...	1565.97	17267.08	389.92	26.02	710.47	3377.84	4088.23	374.07	180.17	37.01
2	Bihar	180424.25	95053.36	135.55	0.13	135.79	83.27	33619.25	2319.19	4038.64	...	5.62	5536.29	6750.22	1.57	5468.52	12105.66	17573.54	5227.58	547.11	405.17
0	Andhra Pradesh	128980.52	98.80	9932.71	13523.62	23456.52	9191.32	4393.90	6659.47	0.00	...	18.19	87223.49	8428.76	18274.69	19921.48	5587.32	25508.58	36.95	883.17	5951.31
1	Assam	121967.69	3338.05	0.00	0.00	0.00	0.00	993.03	0.00	0.00	...	0.00	14176.64	578.36	137.76	3687.31	7868.47	11093.01	3114.10	248.53	136.24
15	Tamil Nadu	112459.14	14.58	21597.19	5327.94	26959.81	12584.76	5257.44	9463.54	0.00	...	94.21	56225.64	11949.53	11232.87	12621.38	8108.47	20729.34	400.05	1211.67	6594.76
13	Punjab	95821.99	159102.06	80.92	0.00	81.22	2677.30	14140.12	9.40	2338.98	...	0.06	8697.93	5531.39	28579.70	2364.05	4219.59	6583.33	1982.38	79.00	29585.49
10	Madhya Pradesh	82342.64	201914.09	68644.32	130.49	68774.82	9778.06	39046.69	153.19	6046.81	...	137165.32	189945.92	2908.57	30653.05	2184.57	7148.39	9332.60	1932.88	1329.12	32454.03
11	Maharashtra	77139.46	48809.70	110830.43	169314.99	280145.41	81080.01	16378.97	9117.78	3.90	...	54582.37	134069.16	24847.84	154319.82	10248.41	7599.86	17848.30	577.13	3895.57	29725.43
7	Jharkhand	69169.78	3039.87	25.08	0.00	25.08	91.64	6615.52	2578.99	518.80	...	3.86	2826.29	156.77	0.13	203.30	2917.82	3120.88	1388.08	114.15	-39.74
8	Karnataka	64104.46	14400.41	31799.10	65646.28	97453.86	22788.74	26161.97	49839.37	0.00	...	3500.48	92527.23	14002.22	37689.74	9544.18	9472.33	19015.80	1457.87	4196.42	3588.23
16	Telangana	63458.94	591.31	23201.61	21712.25	44913.82	5602.85	20348.21	1899.97	0.00	...	2149.76	35293.43	2450.59	29639.37	3776.93	2388.52	6165.06	63.06	452.65	792.60
5	Haryana	39273.38	98464.84	6589.84	0.00	6689.74	35675.69	2905.05	2.20	3921.86	...	0.00	21465.54	6817.17	23062.49	651.00	2097.96	2749.63	536.77	146.99	23854.64
4	Gujarat	32017.85	36657.39	25818.13	5067.82	32246.90	65116.95	19253.28	1853.80	139.80	...	746.19	130724.71	7389.78	94325.68	5665.46	6182.75	11494.93	1637.61	1386.00	42778.35
9	Kerala	27879.92	0.00	109.60	0.00	110.41	0.00	0.37	94.25	0.00	...	0.02	34348.94	323.43	275.31	17260.41	11020.88	28280.89	5.74	2.18	120.53
18	Uttarakhand	11601.72	14841.57	2.80	0.00	2.80	5.55	1630.82	4528.63	1150.91	...	963.98	993.53	2207.31	0.20	359.51	913.37	1310.61	467.78	55.27	663.22
14	Rajasthan	7366.90	109891.84	40726.33	4.64	40862.32	243161.92	47418.96	0.00	17222.25	...	19052.48	142433.47	1234.14	21359.72	962.12	3750.76	4713.29	225.31	1266.12	135865.64
6	Himachal Pradesh	4274.54	17341.53	-32.53	-40.00	-32.53	-11.16	14438.60	253.36	1452.34	...	-22.84	799.25	135.68	3.61	1690.89	1147.95	2875.99	697.72	13.01	322.67

20 rows × 30 columns

fig = px.bar(area_column_data, x='State Name', y=area_column, title="Crop Areas Across States", barmode='stack')
fig.update_layout(
    xaxis_title='State', 
    yaxis_title='Area (1000 ha)', 
    barmode='stack',
    plot_bgcolor='white',  # Set background color to white
    title_x=0.5,  # Center title
    title_y=0.95,  # Adjust title position
    showlegend=True,
    
    bargap=0.05,  # Reduce the gap between bars (bars will be wider)
    bargroupgap=0.1,
    # Make the bars a bit wider
    height=600,  # Set a fixed height for the chart
    width=1000   # Set a fixed width for the chart
)
fig.show()

Rice vs. Wheat Dominance: West Bengal leads in rice cultivation (large blue segment).Uttar Pradesh Punjab and Haryana dominate wheat cultivation (large red segments), reflecting their suitability for wheat due to climate and irrigation facilities.

Uttar Pradesh Madhya Pradesh and Maharashtra show greater crop diversity, with multiple smaller segments representing various crops. Punjab and West Bengal focus heavily on one or two crops (wheat and rice, respectively), indicating a monoculture approach.

States like Madhya Pradesh and Rajasthan allocate significant land to oilseeds . Madhya Pradesh stands out for a high area under pulses .Uttar Pradesh has the largest total agricultural area, spread across rice, wheat, and sugarcane, making it an agriculturally dominant state.

Monoculture Risks: States like Punjab and West Bengal may face ecological and economic risks due to over-dependence on a single crop. Crop diversification policies may be needed.

Regional Specialization: Madhya Pradesh’s balanced crop portfolio makes it a vital contributor to both food grains and oilseeds, ensuring sustainability.